ArticleNPJ digital medicine2024
Establishing the longitudinal hemodynamic mapping framework for wearable-driven coronary digital twins.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- A unified mechano-ionic skin for resolving native electromechanical coupling.Nature communications · 2026Article
- Optimizing Non-invasive Fractional Flow Reserve Estimation with Machine Learning-Enhanced 1D Hemodynamic Modeling.Cardiovascular engineering and technology · 2026Article
- Real-Time Peripheral Revascularization Planning in Chronic Limb Threatening Ischemia Using HarVI: A Digital Twin Approach.Cardiovascular engineering and technology · 2026Article
- Digital twins and digital models of the human circulatory system.Nature reviews bioengineering · 2026Article
- Feel the force: Biomechanical homeostasis of the cardiovascular system.Journal of translational internal medicine · 2026Article
- Emerging Technologies for Exploring the Cellular Mechanisms in Vascular Diseases.International journal of molecular sciences · 2025Review
- Establishing Hemodynamic Convergence Framework for Coronary Digital Twins Under Realistic Dynamic Heart Rates.Physics of fluids (Woodbury, N.Y. : 1994) · 2025Article
- Simulation-based machine learning for real-time assessment of side-branch hemodynamics in coronary bifurcation lesions.The international journal of high performance computing applications · 2025Article
- Article
- Digital twins in healthcare: a comprehensive review and future directions.Frontiers in digital health · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Understanding the evolving nature of coronary hemodynamics is crucial for early disease detection and monitoring progression. We require digital twins that mimic a patient's circulatory system by integrating continuous physiological data and computing hemodynamic patterns over months. Current models match clinical flow measurements but are limited to single heartbeats. To this end, we introduced the longitudinal hemodynamic mapping framework (LHMF), designed to tackle critical challenges: (1) computational intractability of explicit methods; (2) boundary conditions reflecting varying activity states; and (3) accessible computing resources for clinical translation. We show negligible error (0.0002-0.004%) between LHMF and explicit data of 750 heartbeats. We deployed LHMF across traditional and cloud-based platforms, demonstrating high-throughput simulations on heterogeneous systems. Additionally, we established LHMF
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.